Essay Example on Regression Model for Stock Price Dividing

Introduction

The population regression model is: y = v1 + v2 x2 + v3 x3 + e, which is an assumption for an error in e (Aiken, West, & Reno, 1991). Variable e is a constant variance, while the other factors like x(s) are independent variables, which are changes in stock prices (dividing stocks) to make the prices affordable to those small investors (Hayes et al., 2012). Considering that y is a dependent variable the formula below holds:

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(InititialStockPricee - ResultingPrice) = a + v1 (InititialStockPricee - Median ResultingPrice + v2(total duration) + e

Analysis of the First Folder

The analysis for the first folder includes the stock variation on the market before the announcement day, see the attached excel file. Several graphical analyses are showing observed behaviors of the variables, which shows the relationship between the y and x-axis values. For instance, in the study in question line fit plot and normal probability plot have been evident in several single analysis. In fig.2 and fig.1, there is a representation of the normal probability plot checks the coefficient of the data and the errors on it. The general linear model of the study in question remains, (InititialStockPricee - ResultingPrice) = a + v1(InititialStockPricee - Median ResultingPrice + v2(total duration) + e. The values of the correlation coefficient remained as (negative)(positive) 1 (Aiken et al., 1991). There is no area where the coefficient indicates 0 value; this means that are the data provided in the analysis have a linear relationship.

If a straight line was drawn from the graphical results presented in the figures, then it is possible to conclude that most extra points would be negative. This is because the shape of the graph is a U-shaped (random pattern). Therefore until the day of the stock announcement, the prices of stock seem to be stabilized. The announcements of stocks cause demand in shares and stock prices. Changes in stock prices reflect the announcements made in a certain period. The stock splits taken before the announcement day (o day) ought to have a normal pattern that is predetermined with the market situation. Several split factors explain the reason why the results had severals residuals. The most significant split factor is represented by Hexpol AB with consideration of 10:1, followed by 6:1, and 5:1. The smallest constant split fact in the analysis is 2:1, which has happened for a very long time in the market in several listed companies; this is the reason there is a U-shaped (random pattern) in the graphical data represented.

Though the listed companies are affected, the index prices are pretty much unaffected; therefore, the p-value could be used in the null hypothesis, where the coefficients = 0. Hence invalid hypothesis is rejected in the first folder since the p-value is less than the standard value of 0.05; this is because the p-value in the normal distribution is less than 0.005. However, abnormal returns on stocks result from untimely announcements; they only happen when the companies do not respond appropriately to the stock market prices. The trade volume increases when many investors find the shares affordable; the information provided in the stock splits always increases the trading activities (increasing the share prices). The correlation is not statistically significant since the p values are less than 0.05 and have different t-stat values.

Folder 2: Regression Analysis

The result showed that there are consistents of data represented (almost a straight line) since there was data consistency. After the announcement day, the p-value and t-value changed. The t-value is 5.9469, but the other t-value can be structured in two ways (-)(+) 2.7339 having a range of less than 5.9469 (rejected hypothesis). However, abnormal returns on stocks result from untimely announcements, and they only happen when the companies do not respond appropriately to the stock market prices. The trade volume increases when many investors find the shares affordable; the information provided in the stock splits always increases the trading activities. The correlation is not statistically significant since the p values are less than 0.05 and have different t-stat values.

Folder 3: Comparison of liquidity rate of the stock (liquidity to the stock split)

The liquidity rate of the stock measures the trading of shares on any given day; it is determined in regression output to analyses the relationship between the trading pattern and its effect on the stock split. A debtor is expected to pay off current financial obligations without issues concerning external capital (Hayes et., 2012). However, in the study in question, the regression output is shown values of 0.0871 and 0.5643, which both resulted in a top-value less than 0.05.

Finding Descriptions

The data in folder I & 2 shown that stock splits have a higher impact on prices. However, it also is shown that the volume of trade should always match the announcement period. The p-value produced shown that the companies' stock split did not match market prices were not matching with the optimized values Hayes et., 2012). Further, it can also be deduced that p-value can teel the return of the company and, therefore, can be used to determine the trend of stock splits and market prices. The corporate institutions should always be conversant with the announced stock splits and decided the share prices to have for their investors.

References

Aiken, L. S., West, S. G., & Reno, R. R. (1991). Multiple regression: Testing and interpreting interactions. Sage.

Hayes, A. F., Glynn, C. J., & Huge, M. E. (2012). Cautions regarding the interpretation of regression coefficients and hypothesis tests in linear models with interactions. Retrieved from https://doi.org/10.1080/19312458.2012.651415